CCSS: Brain Network Analysis Using Communication Theory

CCSS：使用通信理论进行脑网络分析

• 批准号: 2032709
• 负责人: Tongtong Li
• 金额: $ 47万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032709&HistoricalAwards=false
• 关键词: CCSS; Brain; Network; Analysis; Using

The brain is a communication network, and connectivity between the brain regions generates our minds. Along with improvements in data-gathering techniques, brain researchers are increasingly relying on advanced computational tools to analyze large, complex data sets. At the same time, driven by the revolution in information theory, the communications area has accumulated rich methodologies for system modeling, signal extraction and analysis. Driven by the convergence of information theory and neuroscience, this project aims to develop new techniques for brain analysis by exploiting advanced tools in communications. The new technologies resulted from this project can expand and deepen our understanding of brain functions and performances, and can be applied directly to the diagnosis and treatment of age-related dementia; moreover, by integrating these new technological advances into the undergraduate/graduate curricula and outreach activities, this project has significant impacts on the training of a highly skilled and diverse workforce for communications and computational neuroscience.The goal of the project is to develop innovative methods for quantitative, information-theoretic modeling and characterization of brain connectivity, neuronal activity level, capacities and stability, and to explore the applications of these new techniques in understanding brain functions and human behavioral responses. More specifically, (i) this project introduces an information conservation framework for individual brain regions; for the first time in literature, the neuronal activity level of a brain region is related to its information processing capacity; (ii) this project establishes the conditional equivalence between directed information and dynamic causal modeling, and provides a information-theoretic framework for brain causality analysis; (iii) this project introduces new approaches for quantitative characterization of the stability of individual brain regions, and makes it possible to investigate how brain stability and capacities impact the human behavioral response.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大脑是一个通信网络，大脑区域之间的连接产生了我们的思想。随着数据收集技术的进步，大脑研究人员越来越依赖先进的计算工具来分析大型、复杂的数据集。同时，在信息论革命的推动下，通信领域积累了丰富的系统建模、信号提取和分析方法。在信息论和神经科学融合的推动下，该项目旨在通过利用先进的通信工具来开发大脑分析的新技术。该项目产生的新技术可以扩大和加深我们对大脑功能和表现的理解，并可以直接应用于年龄相关性痴呆的诊断和治疗；此外，通过将这些新技术进步融入本科生/研究生课程和推广活动中，该项目对通信和计算神经科学领域高技能和多样化劳动力的培训产生了重大影响。该项目的目标是开发创新方法对大脑连接、神经元活动水平、能力和稳定性进行定量、信息论建模和表征，并探索这些新技术在理解大脑功能和人类行为反应方面的应用。更具体地说，（i）该项目引入了针对各个大脑区域的信息保存框架；文献首次表明大脑区域的神经元活动水平与其信息处理能力相关； (ii) 该项目建立了定向信息和动态因果模型之间的条件等价性，并为大脑因果关系分析提供了信息理论框架； (iii) 该项目引入了定量表征单个大脑区域稳定性的新方法，并使研究大脑稳定性和能力如何影响人类行为反应成为可能。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。

项目成果

A Mathematical Model for Neuronal Activity and Brain Information Processing Capacity

神经元活动和大脑信息处理能力的数学模型

• DOI: 10.1109/icassp49357.2023.10095567
• 发表时间: 2023-01
• 期刊: Speech and Signal Processing (ICASSP
• 作者: Zheng, Yu;Zhu, David;Ren, Jian;Liu, Taosheng;Friston, Karl;Li, Tongtong
• 通讯作者: Li, Tongtong

Analysis of the Effect of Stretching Exercises on Alzheimer’s Disease

伸展运动对阿尔茨海默病的影响分析

• 发表时间: 2021-01
• 期刊: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
• 作者: Deng, J.;Zhu, D.;Yu, F.;Li, T.
• 通讯作者: Li, T.

Dynamic Functional Connectivity Fading Analysis and Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Normal Control Subjects based on Resting-State fMRI Data

基于静息态 fMRI 数据的阿尔茨海默病、轻度认知障碍和正常对照受试者的动态功能连接衰落分析和分类

• DOI: 10.21926/obm.neurobiol.2002059
• 发表时间: 2020-06
• 期刊: OBM Neurobiology
• 作者: Liang, Yuan;Zheng, Yu;Renli, Brighty;Zhu, David C.;Yu, Fang;Li, Tongtong
• 通讯作者: Li, Tongtong

Brain information processing capacity modeling

大脑信息处理能力建模

• DOI: 10.1038/s41598-022-05870-z
• 发表时间: 2022-02-09
• 期刊: Scientific Reports
• 影响因子: 4.6
• 作者: Li T;Zheng Y;Wang Z;Zhu DC;Ren J;Liu T;Friston K
• 通讯作者: Friston K